Govern AI agents.
Before they act.
AI agents are already in production. The question isn't whether they can act — it's whether you authorized it, whether it can be undone, and whether your auditors will ever know it happened.
Five questions every regulated enterprise must answer before deploying AI agents.
LangGraph, CrewAI, AutoGen, and Google ADK build capable agents. None of them answer these questions. In 2025, an AI agent processed $2.3M in payments without dual-authorization — undetected until monthly reconciliation.
Who authorized it?
Which human delegated authority to this agent for this specific action? At what tier? Under which policy?
Can it be undone?
If a step fails mid-workflow, is there a guaranteed compensation path — or does your team clean up manually?
What data did it see?
Did the LLM process raw PII? Is there a per-session record of what entered the model context?
Where is the audit trail?
Is the log tamper-evident? Can it satisfy SOX §404, HIPAA 45 CFR §164.312, or FedRAMP auditors?
Does it follow policy?
How are rules defined, implemented, and kept synchronized with evolving regulations — without a redeployment?
How TAOS solves this
TAOS sits between every AI agent and every system it calls — a mandatory Policy Enforcement Point at execution time. Authorization, audit, and rollback are enforced before a tool call leaves your network, not reconstructed from telemetry afterward.
An on-premises kernel evaluates policy on each step, brokers vault-stored credentials scoped exactly to that action, and issues stepped-down OBO-style delegation chains so auditors can answer who authorized what, for which agent, under which policy version — the same questions our customers face under SOC 2, SOX, PCI-DSS, HIPAA, and FedRAMP.
Rules your risk and compliance teams can own — versioned, reviewable, and enforced at runtime without redeploying agents. Every action leaves a defensible audit trail before it completes; when something breaks, rollback is built in, not a weekend cleanup project.
Drop TAOS into the stacks you already use — LangGraph, Google ADK, Genkit, AutoGen, CrewAI, or custom workflows — with thin integration at the execution boundary, not a ground-up rewrite of your agents.